Dolomiti's region, province and municipalities

Set up

import geopandas as gpd
import contextily as ctx
import pandas as pd
import matplotlib.pyplot as plt
from functions import *

Dolomiti's regions, provinces and municipalities ranking lists

As mentioned in the introduction, the Dolomites are spread over several Italian regions, and consequently Italian provinces and municipalities. What are these regions, provinces and municipalities? And which of them contains the majority of the peaks? In this notebook we will build three ranking list indicating the percentage of the Dolomites contained in regions, provinces and municipalities involved in the area.

Import Dolomiti's CSV file and convert it in a GeoDataFrame

First, it is essential to import Dolomiti's data (remember that we save it in csv format).

df_dolomities = pd.read_csv('data\df_dolomities.csv')
df_dolomities.head(4)

	Name	Description	geometry	area	centroids
0	Sistema 1 - Pelmo, Croda da Lago	Esplora il Sistema 1 Pelmo, Croda da Lago su<b...	POLYGON Z ((12.121421 46.404879 0, 12.121488 4...	4.315303e+07	POINT (739351.9373813267 5148262.480977141)
1	Sistema 2 - Marmolada	Esplora il Sistema 2 Marmolada su<br>http://ww...	POLYGON Z ((11.823194 46.452963 0, 11.822142 4...	2.205786e+07	POINT (719424.1649959779 5145963.68184033)
2	Sistema 3 - Pale di San Martino, San Lucano, D...	Esplora il Sistema 3 Pale di San Martino, San ...	POLYGON Z ((11.98683 46.317204 0, 11.988004 46...	3.165677e+08	POINT (730679.3390335406 5125674.431470685)
3	Sistema 4 - Dolomiti friulane e d'Oltre Piave	Esplora il Sistema 4 Dolomiti friulane e d'Olt...	POLYGON Z ((12.512045 46.424872 0, 12.512 46.4...	2.146157e+08	POINT (769747.0350640987 5138937.349145058)

type(df_dolomities)

pandas.core.frame.DataFrame

Build the GeoDataFrame from the DataFrame using geopandas' GeoDataFrame():

# retrieve geometry with geopandas' GeoSeries.from_wkt()
geometry = gpd.GeoSeries.from_wkt(df_dolomities['geometry'])
geodf_dolomities = gpd.GeoDataFrame(df_dolomities, geometry=geometry, crs="EPSG:4326")
geodf_dolomities.head(4)

	Name	Description	geometry	area	centroids
0	Sistema 1 - Pelmo, Croda da Lago	Esplora il Sistema 1 Pelmo, Croda da Lago su<b...	POLYGON Z ((12.12142 46.40488 0.00000, 12.1214...	4.315303e+07	POINT (739351.9373813267 5148262.480977141)
1	Sistema 2 - Marmolada	Esplora il Sistema 2 Marmolada su<br>http://ww...	POLYGON Z ((11.82319 46.45296 0.00000, 11.8221...	2.205786e+07	POINT (719424.1649959779 5145963.68184033)
2	Sistema 3 - Pale di San Martino, San Lucano, D...	Esplora il Sistema 3 Pale di San Martino, San ...	POLYGON Z ((11.98683 46.31720 0.00000, 11.9880...	3.165677e+08	POINT (730679.3390335406 5125674.431470685)
3	Sistema 4 - Dolomiti friulane e d'Oltre Piave	Esplora il Sistema 4 Dolomiti friulane e d'Olt...	POLYGON Z ((12.51205 46.42487 0.00000, 12.5120...	2.146157e+08	POINT (769747.0350640987 5138937.349145058)

type(geodf_dolomities)

geopandas.geodataframe.GeoDataFrame

We can also load the GeoDataFrame using a costum function from functions.py:

geodf_dolomities = load_geodf_dolomities('data\I nove Sistemi delle Dolomiti UNESCO.kml')

Get region, provinces and municipality

it is now essential to import data about regions, provinces and municipalities (again, we use a custom function). We use ISTAT data used during the course.

download_extract('data\Limiti01012021_g', 'https://www.istat.it/storage/cartografia/confini_amministrativi/generalizzati/Limiti01012021_g.zip')

regions_path = "data\Limiti01012021_g\Reg01012021_g"
regions = gpd.read_file(regions_path)
regions = regions.to_crs(epsg=4326)[['COD_REG', 'DEN_REG', 'Shape_Area', 'geometry']]

provinces_path = "data\Limiti01012021_g\ProvCM01012021_g"
provinces = gpd.read_file(provinces_path)
provinces = provinces.to_crs(epsg=4326)[['COD_REG', 'COD_PROV', 'DEN_PROV', 'Shape_Area', 'geometry']]

# join data about province with the data about the region they belong to
prov_reg = gpd.sjoin(provinces, regions, how='left', predicate='within', lsuffix='', rsuffix='reg')

municipalities_path = "data\Limiti01012021_g\Com01012021_g"
municipalities = gpd.read_file(municipalities_path)
municipalities = municipalities.to_crs(epsg=4326)[['COD_REG', 'COD_PROV', 'PRO_COM_T', 'COMUNE', 'Shape_Area', 'geometry']]

# join data about minicipalities with the data about the province and region they belong to
prov_reg_mun = gpd.sjoin(municipalities, prov_reg, how='left', predicate='within', lsuffix='', rsuffix='prov')

Join Dolomiti's and regions and provinces data

We ues spatial join with intersect predicate in order to join data about Dolomiti systems and regions and provinces.

geodf_dolomities_provices = gpd.sjoin(geodf_dolomities, prov_reg, how='left', predicate='intersects', lsuffix='dolomities', rsuffix='provinces')
geodf_dolomities_provices = geodf_dolomities_provices[['Name', 'Description', 'geometry', 'area', 'COD_REG_', 'COD_PROV', 'DEN_PROV', 'Shape_Area_', 'DEN_REG', 'Shape_Area_reg']]
geodf_dolomities_provices.head(4)

	Name	Description	geometry	area	COD_REG_	COD_PROV	DEN_PROV	Shape_Area_	DEN_REG	Shape_Area_reg
0	Sistema 1 - Pelmo, Croda da Lago	Esplora il Sistema 1 Pelmo, Croda da Lago su<b...	POLYGON Z ((12.12142 46.40488 0.00000, 12.1214...	4.315303e+07	5	25	Belluno	3.609841e+09	Veneto	1.834355e+10
1	Sistema 2 - Marmolada	Esplora il Sistema 2 Marmolada su<br>http://ww...	POLYGON Z ((11.82319 46.45296 0.00000, 11.8221...	2.205786e+07	5	25	Belluno	3.609841e+09	Veneto	1.834355e+10
1	Sistema 2 - Marmolada	Esplora il Sistema 2 Marmolada su<br>http://ww...	POLYGON Z ((11.82319 46.45296 0.00000, 11.8221...	2.205786e+07	4	22	Trento	6.208170e+09	Trentino-Alto Adige	1.360755e+10
2	Sistema 3 - Pale di San Martino, San Lucano, D...	Esplora il Sistema 3 Pale di San Martino, San ...	POLYGON Z ((11.98683 46.31720 0.00000, 11.9880...	3.165677e+08	5	25	Belluno	3.609841e+09	Veneto	1.834355e+10

Now we have a DataFrame with systems and the regions/provinces containing them.

E.g. Sistema 2 - Marmolada is contained in both Veneto and Trentino Alto Adige, in particular between province of Belluno (Venento) and province of Trento ( Trentino Alto Adige).

Plot Dolomiti systems over regions that contains them

We first extract the Dolomiti's regions' name from the previous data:

dolomiti_regions = geodf_dolomities_provices.DEN_REG.unique()
geo_dolomiti_regions = regions[regions.DEN_REG.isin(dolomiti_regions)]

print("Dolomiti regions: ")
for i in geo_dolomiti_regions.DEN_REG.unique():
    print("-", i, end="\n")

Dolomiti regions: 
- Trentino-Alto Adige
- Veneto
- Friuli Venezia Giulia

Plot data on overall Italian terriroty:

base = regions.to_crs(epsg=4326).plot(
    column='DEN_REG',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7
    )
ctx.add_basemap(base, crs=geo_dolomiti_regions.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


geodf_dolomities.plot(
    column='Name',
    ax=base,
    edgecolor="k",
    alpha=0.75,
    lw=0.1
    )

<AxesSubplot:>

Let's zoom over the regions that contain Dolomiti:

representative_points_region = representative_points_for_labels(geo_dolomiti_regions, 'DEN_REG')
representative_points_region

	index	geometry	names
0	3	POINT (11.22145 46.38468)	Trentino-Alto Adige
1	4	POINT (12.09621 45.73561)	Veneto
2	5	POINT (13.04718 46.11426)	Friuli Venezia Giulia

Plot the zoomed map with the regions name labels:

texts = []

base = geo_dolomiti_regions.to_crs(epsg=4326).plot(
    column='DEN_REG',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.6,
    alpha=0.7
    )
ctx.add_basemap(base, crs=geo_dolomiti_regions.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


geodf_dolomities.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.75,
    lw=0.1
    )

for x, y, label in zip(representative_points_region.to_crs(epsg=4326).geometry.x, representative_points_region.to_crs(epsg=4326).geometry.y, representative_points_region["names"]):
    texts.append(plt.text(x, y, label, fontsize = 14, color="#393e41", fontweight='regular', fontfamily='sans-serif', horizontalalignment='center'))

Which region contains the majority of the Dolomiti's peaks?

list_reg = []
list_dol_area = []
list_dol_percentage = []

for i in geo_dolomiti_regions.DEN_REG.unique():
    temp = regions_systems_percentage(i, geodf_dolomities)
    
    list_reg.append(temp[0])
    list_dol_area.append(temp[1])
    list_dol_percentage.append(temp[2])

45.59% of the Dolomiti's peaks is in Trentino-Alto Adige with 646.86 km²
41.2% of the Dolomiti's peaks is in Veneto with 584.6 km²
13.2% of the Dolomiti's peaks is in Friuli Venezia Giulia with 187.35 km²

Save results:

rank_dolomiti_reg = pd.DataFrame(list(zip(list_reg, list_dol_area, list_dol_percentage)),
               columns =['region', 'dolomiti_area_km', 'percentage_over_total_dolimiti_area'])

rank_dolomiti_reg

	region	dolomiti_area_km	percentage_over_total_dolimiti_area
0	Trentino-Alto Adige	646.86	45.59
1	Veneto	584.60	41.20
2	Friuli Venezia Giulia	187.35	13.20

The region with the majority of Dolomiti is Trentino-Alto Adige, followed by Veneto and Friuli Venezia Giulia.

As we can see from the previous plots, only few territories of these region effectively contain Dolomiti systems, let's indagate through their provinces.

Plot Dolomiti systems over provinces containing them

dolomiti_provinces = geodf_dolomities_provices.DEN_PROV.unique()
geo_dolomiti_provinces = provinces[provinces.DEN_PROV.isin(dolomiti_provinces)]

print("Dolomiti provinces: ")
for i in geo_dolomiti_provinces.DEN_PROV.unique():
    print("-", i, end="\n")

Dolomiti provinces: 
- Bolzano
- Trento
- Belluno
- Udine
- Pordenone

Get the provincial capital using the city_coordinates_label custom function:

geo_capoluighi = city_coordinates_label(geo_dolomiti_provinces, 'DEN_PROV')
geo_capoluighi

	geometry	DEN_PROV
0	POINT (11.35346 46.49528)	Bolzano
1	POINT (11.11929 46.07005)	Trento
2	POINT (12.21275 46.14098)	Belluno
3	POINT (13.23807 46.06452)	Udine
4	POINT (12.64453 45.96493)	Pordenone

Plot the Dolomiti systems over their provinces with provincial capitals labels:

base = geo_dolomiti_provinces.to_crs(epsg=4326).plot(
    column='DEN_PROV',
    categorical=True,
    legend=True,
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7
    )
ctx.add_basemap(base, crs=geo_dolomiti_provinces.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


geodf_dolomities.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.70,
    lw=0.1,
    )

for x, y, label in zip(geo_capoluighi.to_crs(epsg=4326).geometry.x, geo_capoluighi.to_crs(epsg=4326).geometry.y, geo_capoluighi['DEN_PROV']):
    texts.append(plt.text(x, y, label, fontsize = 12, color="#393e41", fontweight='regular', fontfamily='sans-serif', horizontalalignment='center'))

Dolomiti's province and municipalities analysis

We have just seen the ranking for the Dolomiti's regions. The following sections will instead determine which provinces and municipalities contain the majority of the territory of the Dolomites.

Province of Trento

The province of Trento, together with the province of Bolzano, constitute the region with the higher percentage of Dolomiti's peaks: Trentino Alto-Adige.

Get province GeoDataFrame and plot it:

province_TN = provinces[provinces.DEN_PROV == 'Trento']
province_TN

	COD_REG	COD_PROV	DEN_PROV	Shape_Area	geometry
21	4	22	Trento	6.208170e+09	POLYGON ((11.82408 46.50408, 11.81545 46.50032...

province_TN.plot()

<AxesSubplot:>

Extract Dolomiti systems of province of Trento and plot them:

dolomities_TN = geodf_dolomities_provices[geodf_dolomities_provices.DEN_PROV == province_TN.DEN_PROV.values[0]]
dolomities_TN

	Name	Description	geometry	area	COD_REG_	COD_PROV	DEN_PROV	Shape_Area_	DEN_REG	Shape_Area_reg
1	Sistema 2 - Marmolada	Esplora il Sistema 2 Marmolada su<br>http://ww...	POLYGON Z ((11.82319 46.45296 0.00000, 11.8221...	2.205786e+07	4	22	Trento	6.208170e+09	Trentino-Alto Adige	1.360755e+10
2	Sistema 3 - Pale di San Martino, San Lucano, D...	Esplora il Sistema 3 Pale di San Martino, San ...	POLYGON Z ((11.98683 46.31720 0.00000, 11.9880...	3.165677e+08	4	22	Trento	6.208170e+09	Trentino-Alto Adige	1.360755e+10
6	Sistema 7 - Sciliar-Catinaccio, Latemar	Esplora il Sistema 7 Sciliar-Catinaccio, Latem...	POLYGON Z ((11.51976 46.48659 0.00000, 11.5202...	9.302092e+07	4	22	Trento	6.208170e+09	Trentino-Alto Adige	1.360755e+10
8	Sistema 9 - Dolomiti di Brenta	Esplora il Sistema 9 Dolomiti di Brenta su<br>...	POLYGON Z ((10.97380 46.24359 0.00000, 10.9735...	1.113544e+08	4	22	Trento	6.208170e+09	Trentino-Alto Adige	1.360755e+10

print('province of ', province_TN.DEN_PROV.values[0], ' contains ', len(dolomities_TN.Name.unique()), " Dolomiti systems:", sep="")
for i in dolomities_TN.Name.unique():
    print("-", i, end="\n")

province of Trento contains 4 Dolomiti systems:
- Sistema 2 - Marmolada
- Sistema 3 - Pale di San Martino, San Lucano, Dolomiti Bellunesi, Vette Feltrine
- Sistema 7 - Sciliar-Catinaccio, Latemar
- Sistema 9 - Dolomiti di Brenta

base = province_TN.to_crs(epsg=4326).plot(
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7
    )
ctx.add_basemap(base, zoom=12, crs=province_TN.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


dolomities_TN.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.70,
    lw=0.1,
    )

<AxesSubplot:>

Clip the Dolomiti systems geometry in order to have only the territory inside the province boudaries

As we can see from the plot, the province hardly ever contains the whole territory of a system. Using geopandas' clip() we then crop the area of each system on the province boundaries.

dolomities_TN_clipped = dolomities_TN.clip(province_TN)

base = province_TN.to_crs(epsg=4326).plot(
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7
    )
ctx.add_basemap(base, zoom=12, crs=province_TN.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


dolomities_TN_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.70,
    lw=0.1,
    )

<AxesSubplot:>

Recalculate clipped Dolomiti's territory area

As a consequence of clipping, it is necessary to recalculate the area of each system:

dolomities_TN_clipped_areas= dolomities_TN_clipped.to_crs(epsg=32632)
dolomities_TN_clipped_areas["area"] = dolomities_TN_clipped_areas['geometry'].area
dolomities_TN_clipped_areas

	Name	Description	geometry	area	COD_REG_	COD_PROV	DEN_PROV	Shape_Area_	DEN_REG	Shape_Area_reg
8	Sistema 9 - Dolomiti di Brenta	Esplora il Sistema 9 Dolomiti di Brenta su<br>...	POLYGON ((652158.161 5122687.179, 652162.795 5...	1.113544e+08	4	22	Trento	6.208170e+09	Trentino-Alto Adige	1.360755e+10
2	Sistema 3 - Pale di San Martino, San Lucano, D...	Esplora il Sistema 3 Pale di San Martino, San ...	MULTIPOLYGON (((725381.201 5115301.937, 725290...	4.579665e+07	4	22	Trento	6.208170e+09	Trentino-Alto Adige	1.360755e+10
1	Sistema 2 - Marmolada	Esplora il Sistema 2 Marmolada su<br>http://ww...	POLYGON ((718275.208 5143106.310, 718245.759 5...	1.148806e+07	4	22	Trento	6.208170e+09	Trentino-Alto Adige	1.360755e+10
6	Sistema 7 - Sciliar-Catinaccio, Latemar	Esplora il Sistema 7 Sciliar-Catinaccio, Latem...	MULTIPOLYGON (((700185.820 5141183.564, 700337...	3.898385e+07	4	22	Trento	6.208170e+09	Trentino-Alto Adige	1.360755e+10

Sum the Dolomiti's areas in Trentino:

dolomiti_TN_area = sum(dolomities_TN_clipped_areas.area)
print(round(dolomiti_TN_area/10**6, 2), " km\u00b2 of Dolomiti are in province of Trento", sep="")

207.62 km² of Dolomiti are in province of Trento

Let's plot the Dolomiti's areas as difference difference overlay, to give an idea of their impact over the province territory.

dolomiti_TN_overlay = province_TN.to_crs(epsg=32632).overlay(dolomities_TN_clipped.to_crs(epsg=32632), how="difference")
base = province_TN.to_crs(epsg=4326).plot(
    figsize=(15,15),
    color= "white",
    edgecolor="k",
    lw=0.7,
    alpha=0.7
    )
dolomiti_TN_overlay.to_crs(epsg=4326).plot(ax=base)

<AxesSubplot:>

Proportion of Dolomiti's territory in province of Trento

It is possible to calculate the proportion of Dolomiti over the province of Trento by getting the Dolomiti's area for the province and dividing it for the whole area of the Dolomiti.

dolomiti_TN_area = sum(dolomities_TN_clipped_areas.area)
overal_dolomiti_area = sum(geodf_dolomities['area'])

persentage_dolomiti_TN = (dolomiti_TN_area/overal_dolomiti_area)*100

print("Province of Trento contains the ", round(persentage_dolomiti_TN, 2), "% of the Dolomiti's terriotory", sep="")

Province of Trento contains the 14.63% of the Dolomiti's terriotory

What proportion of each system does the province contain?

As mentioned earlier the province hardly ever contains the whole territory of a system, let's see how much system's territory it contains in percentage.

# function to obtain the percentage of each Dolomitic system in the province
def province_systems_percentage(province, system):
    
    # get the province GeoDataFrame
    province_geodf = provinces[provinces.DEN_PROV == province]
    
    # get the system GeoDataFrame
    system_geodf = geodf_dolomities[geodf_dolomities.Name == system]
    # calculate system's area
    system_area = geodf_dolomities[geodf_dolomities.Name == system]['area'].values[0]
    
    # clip the system shape on the province boundaries recalculating area
    system_area_clipped = system_geodf.to_crs(epsg=32632).clip(province_geodf.to_crs(epsg=32632)).area
    # calculate the percentage
    percentage_of_system = (system_area_clipped.values[0]/system_area)*100

    print(round(percentage_of_system, 2), "% of the", " '",system,"' ", "is in province of ", province, sep="")

for index, row in dolomities_TN.iterrows():
    province_systems_percentage(row.DEN_PROV, row.Name)

52.08% of the 'Sistema 2 - Marmolada' is in province of Trento
14.47% of the 'Sistema 3 - Pale di San Martino, San Lucano, Dolomiti Bellunesi, Vette Feltrine' is in province of Trento
41.91% of the 'Sistema 7 - Sciliar-Catinaccio, Latemar' is in province of Trento
100.0% of the 'Sistema 9 - Dolomiti di Brenta' is in province of Trento

As we can see also in the previous plot, the province of Trento contains the whole terriotry of Sistema 9, half of Sistema 2 and other portions of Sistema 3 e Sistema 7.

Dolomiti's municipalities of province of Trento

The province of Trento contains four systems, but these cover only a small part of its territory. Let's find out which municipalities of the province contain Dolomiti's areas.

Join geodf_dolomities with provinces and regions

Using spatial join with intersect predicate.

geodf_dolomities_municipalities = gpd.sjoin(geodf_dolomities, prov_reg_mun, how='left', predicate='intersects', lsuffix='dolomities', rsuffix='mun')
geodf_dolomities_municipalities = geodf_dolomities_municipalities[['Name', 'Description', 'geometry', 'area', 'COD_REG', 'COD_PROV_', 'PRO_COM_T', 'COMUNE', 'Shape_Area', 'DEN_PROV', 'DEN_REG']]
geodf_dolomities_municipalities.head(4)

	Name	Description	geometry	area	COD_REG	COD_PROV_	PRO_COM_T	COMUNE	Shape_Area	DEN_PROV	DEN_REG
0	Sistema 1 - Pelmo, Croda da Lago	Esplora il Sistema 1 Pelmo, Croda da Lago su<b...	POLYGON Z ((12.12142 46.40488 0.00000, 12.1214...	4.315303e+07	5	25	025073	Val di Zoldo	1.414058e+08	Belluno	Veneto
0	Sistema 1 - Pelmo, Croda da Lago	Esplora il Sistema 1 Pelmo, Croda da Lago su<b...	POLYGON Z ((12.12142 46.40488 0.00000, 12.1214...	4.315303e+07	5	25	025069	Zoppè di Cadore	4.585119e+06	Belluno	Veneto
0	Sistema 1 - Pelmo, Croda da Lago	Esplora il Sistema 1 Pelmo, Croda da Lago su<b...	POLYGON Z ((12.12142 46.40488 0.00000, 12.1214...	4.315303e+07	5	25	025066	Vodo Cadore	4.671745e+07	Belluno	Veneto
0	Sistema 1 - Pelmo, Croda da Lago	Esplora il Sistema 1 Pelmo, Croda da Lago su<b...	POLYGON Z ((12.12142 46.40488 0.00000, 12.1214...	4.315303e+07	5	25	025007	Borca di Cadore	2.669519e+07	Belluno	Veneto

In this Dataset we have all the municipalities involved in the territory with data on the corresponding province, region and Dolomiti systems.

E.g. Val di Zoldo is a municipality in province of Belluno, in Veneto, and it contains a portion of Sistema 1 - Pelmo, Croda da Lago.

Get municipalities of province of Trento

code_TN = province_TN.COD_PROV.values[0]
code_TN

22

municipalities_TN = municipalities[municipalities.COD_PROV == code_TN]
municipalities_TN.head(4)

	COD_REG	COD_PROV	PRO_COM_T	COMUNE	Shape_Area	geometry
2871	4	22	022001	Ala	1.200270e+08	POLYGON ((11.00066 45.82692, 11.00103 45.82634...
2872	4	22	022002	Albiano	9.804277e+06	POLYGON ((11.20754 46.15620, 11.20994 46.15454...
2873	4	22	022003	Aldeno	9.035749e+06	POLYGON ((11.10303 45.99093, 11.11333 45.98973...
2874	4	22	022005	Andalo	1.141823e+07	MULTIPOLYGON (((10.95317 46.13797, 10.95315 46...

municipalities_TN.plot()

<AxesSubplot:>

Get municipalities that contain Dolomiti's territory

dolomiti_mun = geodf_dolomities_municipalities.COMUNE.unique()
geo_dolomiti_TN_mun = municipalities_TN[municipalities_TN.COMUNE.isin(dolomiti_mun)]

print("Municipalities in province of Trento that contain Dolomiti: ")
for i in geo_dolomiti_TN_mun.COMUNE.unique():
    print("-", i, end="\n")

Municipalities in province of Trento that contain Dolomiti: 
- Andalo
- Campitello di Fassa
- Campodenno
- Canazei
- Cavedago
- Imer
- Mazzin
- Mezzano
- Moena
- Molveno
- Predazzo
- Sagron Mis
- Soraga di Fassa
- Spormaggiore
- Sporminore
- Stenico
- Comano Terme
- San Lorenzo Dorsino
- Primiero San Martino di Castrozza
- Tre Ville
- Ville d'Anaunia
- San Giovanni di Fassa

Plot these municipalities along with Dolomiti systems:

base = municipalities_TN.plot(
    column='COD_PROV',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="#413A3B",
    lw=0.6,
    alpha=0.5)

ctx.add_basemap(base, crs=municipalities_TN.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)

geo_dolomiti_TN_mun.to_crs(epsg=4326).plot(
    column='COMUNE',
    figsize=(15,15),
    cmap="Paired",
    edgecolor="k",
    lw=0.2,
    alpha=0.4,
    ax=base
    )

dolomities_TN_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.85,
    lw=1,
    )

<AxesSubplot:>

As we can notice, not all shapes seem to touch a Dolomiti system. This is due to the fact that their municipality's geometry is Multipolygon. Let's check this point:

multipoly_mun = geo_dolomiti_TN_mun[geo_dolomiti_TN_mun.geometry.geom_type == 'MultiPolygon']
multipoly_mun

	COD_REG	COD_PROV	PRO_COM_T	COMUNE	Shape_Area	geometry
2874	4	22	022005	Andalo	1.141823e+07	MULTIPOLYGON (((10.95317 46.13797, 10.95315 46...
2929	4	22	022097	Imer	2.782916e+07	MULTIPOLYGON (((11.79596 46.15639, 11.79899 46...
2983	4	22	022176	Soraga di Fassa	2.021369e+07	MULTIPOLYGON (((11.69002 46.39827, 11.69068 46...
2988	4	22	022182	Stenico	4.929662e+07	MULTIPOLYGON (((10.84194 46.11319, 10.84293 46...
3010	4	22	022228	Comano Terme	6.778564e+07	MULTIPOLYGON (((10.91873 46.06429, 10.91921 46...
3029	4	22	022247	Tre Ville	8.258386e+07	MULTIPOLYGON (((10.78454 46.08734, 10.78580 46...

Plot Multiplygon Dolomiti's municipalities:

base = municipalities_TN.plot(
    column='COD_PROV',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.5)

ctx.add_basemap(base, crs=municipalities_TN.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)

multipoly_mun.to_crs(epsg=4326).plot(
    column='COMUNE',
    figsize=(15,15),
    cmap="Set3",
    edgecolor="k",
    lw=0.2,
    alpha=0.7,
    ax=base
    )

dolomities_TN_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.75,
    lw=0.8,
    )

<AxesSubplot:>

Which is the municiplaity that contains the highest area of Dolomiti?

First, we create the Dolomiti area by dissolving the systems geometries.

# we add a column with the same value for each system in order to dissolve on this column
geodf_dolomities['territory']='dolomiti'

dolomiti = geodf_dolomities[['territory', 'geometry']]
overal_dolomiti = dolomiti.to_crs(epsg=4326).dissolve(by='territory')

Check the total area:

sum(geodf_dolomities['area'])

1418804775.9299965

Finnaly we build a function to obtain the percentage of Dolomiti territory in the involved municipalities:

def mun_systems_area(mun):
    
    # get municipality GeoDataFrame
    mun_geodf = municipalities[municipalities.COMUNE == mun]
    
    # calculate Dolomiti's total area
    system_geodf = geodf_dolomities
    systems_area = sum(geodf_dolomities['area'])
    
    # clip the system shape on the minicipality boundaries recalculating area
    system_area_clipped = overal_dolomiti.to_crs(epsg=32632).clip(mun_geodf.to_crs(epsg=32632)).area
    # calculate percentage
    percentage_dolomiti = (system_area_clipped/systems_area)*100

    print("'", mun, "'", " contains ", round(system_area_clipped.values[0]/10**6, 3), " km\u00b2 of Dolomities.", "and the ", round(percentage_dolomiti.values[0], 4), "% of the overall Dolomities territory", sep="")
    
    return [mun, system_area_clipped.values[0], percentage_dolomiti.values[0]]

We add the results of the function to lists to then compose a Dataset. This Dataset will contain the minicipality's name, the Dolomiti area contained in its boundaries and its percentage over the total Dolomit area.

list_mun = []
list_area = []
list_percentage = []

for index, row in geo_dolomiti_TN_mun.iterrows():
    temp = mun_systems_area(row.COMUNE)

    list_mun.append(temp[0])
    list_area.append(temp[1])
    list_percentage.append(temp[2])

'Andalo' contains 0.036 km² of Dolomities.and the 0.0026% of the overall Dolomities territory
'Campitello di Fassa' contains 2.175 km² of Dolomities.and the 0.1533% of the overall Dolomities territory
'Campodenno' contains 5.442 km² of Dolomities.and the 0.3836% of the overall Dolomities territory
'Canazei' contains 7.163 km² of Dolomities.and the 0.5048% of the overall Dolomities territory
'Cavedago' contains 0.348 km² of Dolomities.and the 0.0245% of the overall Dolomities territory
'Imer' contains 0.189 km² of Dolomities.and the 0.0133% of the overall Dolomities territory
'Mazzin' contains 4.46 km² of Dolomities.and the 0.3144% of the overall Dolomities territory
'Mezzano' contains 2.874 km² of Dolomities.and the 0.2026% of the overall Dolomities territory
'Moena' contains 3.533 km² of Dolomities.and the 0.249% of the overall Dolomities territory
'Molveno' contains 14.443 km² of Dolomities.and the 1.018% of the overall Dolomities territory
'Predazzo' contains 7.994 km² of Dolomities.and the 0.5635% of the overall Dolomities territory
'Sagron Mis' contains 1.411 km² of Dolomities.and the 0.0995% of the overall Dolomities territory
'Soraga di Fassa' contains 0.334 km² of Dolomities.and the 0.0236% of the overall Dolomities territory
'Spormaggiore' contains 9.489 km² of Dolomities.and the 0.6688% of the overall Dolomities territory
'Sporminore' contains 2.989 km² of Dolomities.and the 0.2107% of the overall Dolomities territory
'Stenico' contains 8.044 km² of Dolomities.and the 0.567% of the overall Dolomities territory
'Comano Terme' contains 4.291 km² of Dolomities.and the 0.3024% of the overall Dolomities territory
'San Lorenzo Dorsino' contains 36.7 km² of Dolomities.and the 2.5867% of the overall Dolomities territory
'Primiero San Martino di Castrozza' contains 41.322 km² of Dolomities.and the 2.9124% of the overall Dolomities territory
'Tre Ville' contains 17.531 km² of Dolomities.and the 1.2356% of the overall Dolomities territory
'Ville d'Anaunia' contains 12.04 km² of Dolomities.and the 0.8486% of the overall Dolomities territory
'San Giovanni di Fassa' contains 24.812 km² of Dolomities.and the 1.7488% of the overall Dolomities territory

df_mun_TN = pd.DataFrame(list(zip(list_mun, list_area, list_percentage)),
               columns =['municipality', 'Dolomiti_area', 'percentage_over_total_Dolimiti_area'])

sorted_df = df_mun_TN.sort_values(by=['Dolomiti_area'], ascending=False)
sorted_df.head(5)

	municipality	Dolomiti_area	percentage_over_total_Dolimiti_area
18	Primiero San Martino di Castrozza	4.132196e+07	2.912448
17	San Lorenzo Dorsino	3.670043e+07	2.586715
21	San Giovanni di Fassa	2.481193e+07	1.748791
19	Tre Ville	1.753142e+07	1.235647
9	Molveno	1.444332e+07	1.017992

We also add the province column to distinguish them from upcoming municipalities.

sorted_df["province"] = 'Trento'

Which is the municipality with the highest area of Domolimiti in the province of Trento?

highest_dolomiti_area_mun = sorted_df[sorted_df.Dolomiti_area == sorted_df.Dolomiti_area.max()]
primiero = municipalities[municipalities.COMUNE == highest_dolomiti_area_mun.municipality.values[0]]
primiero

	COD_REG	COD_PROV	PRO_COM_T	COMUNE	Shape_Area	geometry
3027	4	22	022245	Primiero San Martino di Castrozza	1.996259e+08	POLYGON ((11.77169 46.35838, 11.77438 46.35824...

base = municipalities_TN.plot(
    column='COD_PROV',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.5)

ctx.add_basemap(base, crs=municipalities_TN.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)

primiero.to_crs(epsg=4326).plot(
    figsize=(15,15),
    color="orange",
    edgecolor="k",
    lw=0.6,
    alpha=0.9,
    ax=base
    )

dolomities_TN_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.75,
    lw=0.1,
    )

<AxesSubplot:>

Primiero San Martino di Castrozza is the municipality with the highest area of Domolimiti in the province of Trento.

Let's check if areas and percentage of the municipalities sum up to that of the province of Trento:

round(dolomiti_TN_area, -2) == round(sum(sorted_df.Dolomiti_area), -2)

True

round(persentage_dolomiti_TN, -2) == round(sum(sorted_df.percentage_over_total_Dolimiti_area), -2)

True

Province of Bolzano

Province of Bolzano is the other procince of Trentino-Alto Adige, which represents the region with the largest area of Dolomiti mountains.

province_BZ = provinces[provinces.DEN_PROV == 'Bolzano']
province_BZ

	COD_REG	COD_PROV	DEN_PROV	Shape_Area	geometry
20	4	21	Bolzano	7.399378e+09	POLYGON ((12.20511 47.08653, 12.20668 47.08627...

province_BZ.plot()

<AxesSubplot:>

Extract Dolomiti systems of province of Bolzano:

dolomities_BZ = geodf_dolomities_provices[geodf_dolomities_provices.DEN_PROV == 'Bolzano']
dolomities_BZ

	Name	Description	geometry	area	COD_REG_	COD_PROV	DEN_PROV	Shape_Area_	DEN_REG	Shape_Area_reg
4	Sistema 5 - Dolomiti settentrionali	Esplora il Sistema 5 Dolomiti settentrionali s...	MULTIPOLYGON Z (((12.16274 46.57915 0.00000, 1...	5.360157e+08	4	21	Bolzano	7.399378e+09	Trentino-Alto Adige	1.360755e+10
5	Sistema 6 - Puez-Odle	Esplora il Sistema 6 Puez-Odle su<br>http://ww...	POLYGON Z ((11.83423 46.66379 0.00000, 11.8333...	7.930332e+07	4	21	Bolzano	7.399378e+09	Trentino-Alto Adige	1.360755e+10
6	Sistema 7 - Sciliar-Catinaccio, Latemar	Esplora il Sistema 7 Sciliar-Catinaccio, Latem...	POLYGON Z ((11.51976 46.48659 0.00000, 11.5202...	9.302092e+07	4	21	Bolzano	7.399378e+09	Trentino-Alto Adige	1.360755e+10
7	Sistema 8 - Bletterbach	Esplora il Sistema 8 Bletterbach su<br>http://...	POLYGON Z ((11.41907 46.36664 0.00000, 11.4190...	2.716007e+06	4	21	Bolzano	7.399378e+09	Trentino-Alto Adige	1.360755e+10

base = province_BZ.to_crs(epsg=4326).plot(
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7
    )
ctx.add_basemap(base, zoom=12, crs=province_BZ.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


dolomities_BZ.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.70,
    lw=0.1,
    )

<AxesSubplot:>

Clip the Dolomiti systems geometry in order to have only the territory inside the province boudaries

dolomities_BZ_clipped = dolomities_BZ.clip(province_BZ)

base = province_BZ.to_crs(epsg=4326).plot(
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7
    )
ctx.add_basemap(base, zoom=12, crs=province_BZ.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


dolomities_BZ_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.70,
    lw=0.1,
    )

<AxesSubplot:>

Recalculate clipped Dolomiti's territory area

dolomities_BZ_clipped_areas = dolomities_BZ_clipped.to_crs(epsg=32632)
dolomities_BZ_clipped_areas["area"] = dolomities_BZ_clipped_areas['geometry'].area 
dolomities_BZ_clipped_areas

	Name	Description	geometry	area	COD_REG_	COD_PROV	DEN_PROV	Shape_Area_	DEN_REG	Shape_Area_reg
7	Sistema 8 - Bletterbach	Esplora il Sistema 8 Bletterbach su<br>http://...	POLYGON ((686070.959 5137628.513, 686071.420 5...	2.716007e+06	4	21	Bolzano	7.399378e+09	Trentino-Alto Adige	1.360755e+10
6	Sistema 7 - Sciliar-Catinaccio, Latemar	Esplora il Sistema 7 Sciliar-Catinaccio, Latem...	MULTIPOLYGON (((701176.461 5144473.670, 701115...	5.403707e+07	4	21	Bolzano	7.399378e+09	Trentino-Alto Adige	1.360755e+10
4	Sistema 5 - Dolomiti settentrionali	Esplora il Sistema 5 Dolomiti settentrionali s...	MULTIPOLYGON (((747258.034 5167424.443, 747224...	3.031830e+08	4	21	Bolzano	7.399378e+09	Trentino-Alto Adige	1.360755e+10
5	Sistema 6 - Puez-Odle	Esplora il Sistema 6 Puez-Odle su<br>http://ww...	POLYGON ((716892.208 5171663.164, 716967.716 5...	7.930332e+07	4	21	Bolzano	7.399378e+09	Trentino-Alto Adige	1.360755e+10

Sum the Dolomiti's areas in province of Bolzano:

dolomiti_BZ_area = sum(dolomities_BZ_clipped_areas.area)
print(round(dolomiti_BZ_area/10**6, 2), " km\u00b2 of Dolomiti are in province of Bolzano", sep="")

439.24 km² of Dolomiti are in province of Bolzano

The province of Bolzano seems to include much more Dolomiti's territory than the province of Trento.

With the overlay difference prlot it is possible to notice the higher impact on the province's territory:

dolomiti_BZ_overlay = province_BZ.to_crs(epsg=32632).overlay(dolomities_BZ_clipped.to_crs(epsg=32632), how="difference")
base = province_BZ.to_crs(epsg=4326).plot(
    figsize=(15,15),
    color= "white",
    edgecolor="k",
    lw=0.7,
    alpha=0.7
    )
dolomiti_BZ_overlay.to_crs(epsg=4326).plot(ax=base)

<AxesSubplot:>

Proportion of Dolomiti's territory in province of Bolzano

dolomiti_BZ_area = sum(dolomities_BZ_clipped_areas.area)
overal_dolomiti_area = sum(geodf_dolomities['area'])

persentage_dolomiti_BZ = (dolomiti_BZ_area/overal_dolomiti_area)*100

print("Province of Trento contains the ", round(persentage_dolomiti_BZ, 2), "% of the Dolomiti's terriotory", sep="")

Province of Trento contains the 30.96% of the Dolomiti's terriotory

As previously assumed, the province contains twice as much Dolomiti's peaks as the previous one.

Proportion of each Dolomiti system in province of Trento

We can use the previous defined function:

for index, row in dolomities_BZ.iterrows():
    province_systems_percentage(row.DEN_PROV, row.Name)

56.56% of the 'Sistema 5 - Dolomiti settentrionali' is in province of Bolzano
100.0% of the 'Sistema 6 - Puez-Odle' is in province of Bolzano
58.09% of the 'Sistema 7 - Sciliar-Catinaccio, Latemar' is in province of Bolzano
100.0% of the 'Sistema 8 - Bletterbach' is in province of Bolzano

The province contains two systems in its totality and more than half of two other systems. Province of Bolzano wins over province of Trento.

Dolomiti's municipalities of province of Bolzano

Get municipalities of province of Bolzano:

code_BZ = province_BZ.COD_PROV.values[0]
code_BZ

21

municipalities_BZ = municipalities[municipalities.COD_PROV == code_BZ]
municipalities_BZ.head(4)

	COD_REG	COD_PROV	PRO_COM_T	COMUNE	Shape_Area	geometry
2755	4	21	021001	Aldino	6.276794e+07	POLYGON ((11.33993 46.40130, 11.35272 46.39816...
2756	4	21	021002	Andriano	5.290305e+06	POLYGON ((11.24288 46.52772, 11.24392 46.52792...
2757	4	21	021003	Anterivo	1.117156e+07	POLYGON ((11.34675 46.29452, 11.35893 46.29612...
2758	4	21	021004	Appiano sulla strada del vino	5.817631e+07	POLYGON ((11.20784 46.52054, 11.21153 46.52043...

municipalities_BZ.plot()

<AxesSubplot:>

Get municipalities that contain Dolomiti's territory

geo_dolomiti_BZ_mun = municipalities_BZ[municipalities_BZ.COMUNE.isin(dolomiti_mun)]

print("Municipalities in province of Bolzano that contain Dolomiti: ")
for i in geo_dolomiti_BZ_mun.COMUNE.unique():
    print("-", i, end="\n")

Municipalities in province of Bolzano that contain Dolomiti: 
- Aldino
- Badia
- Braies
- Castelrotto
- Corvara in Badia
- Dobbiaco
- Fiè allo Sciliar
- Funes
- Marebbe
- Nova Levante
- Nova Ponente
- Ortisei
- San Candido
- San Martino in Badia
- Santa Cristina Valgardena
- Selva di Val Gardena
- Sesto
- Tires
- Valdaora
- La Valle

base = municipalities_BZ.plot(
    column='COD_PROV',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.6,
    alpha=0.5)

ctx.add_basemap(base, crs=municipalities_BZ.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)

geo_dolomiti_BZ_mun.to_crs(epsg=4326).plot(
    column='COMUNE',
    figsize=(15,15),
    cmap="Paired",
    edgecolor="k",
    lw=0.2,
    alpha=0.4,
    ax=base
    )

dolomities_BZ_clipped.to_crs(epsg=4326).plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.85,
    lw=1,
    )

<AxesSubplot:>

Which is the municiplaity that contains the highest area of Dolomiti?

We use the previous defined function to calculate municipalities Dolomiti's areas and percentage on the overall Dolomiti's terriotry.

list_mun_BZ = []
list_area_BZ = []
list_percentage_BZ = []

for index, row in geo_dolomiti_BZ_mun.iterrows():
    temp = mun_systems_area(row.COMUNE)

    list_mun_BZ.append(temp[0])
    list_area_BZ.append(temp[1])
    list_percentage_BZ.append(temp[2])

'Aldino' contains 2.716 km² of Dolomities.and the 0.1914% of the overall Dolomities territory
'Badia' contains 22.984 km² of Dolomities.and the 1.62% of the overall Dolomities territory
'Braies' contains 54.775 km² of Dolomities.and the 3.8606% of the overall Dolomities territory
'Castelrotto' contains 9.882 km² of Dolomities.and the 0.6965% of the overall Dolomities territory
'Corvara in Badia' contains 5.279 km² of Dolomities.and the 0.372% of the overall Dolomities territory
'Dobbiaco' contains 59.918 km² of Dolomities.and the 4.2231% of the overall Dolomities territory
'Fiè allo Sciliar' contains 10.91 km² of Dolomities.and the 0.7689% of the overall Dolomities territory
'Funes' contains 15.7 km² of Dolomities.and the 1.1066% of the overall Dolomities territory
'Marebbe' contains 102.101 km² of Dolomities.and the 7.1963% of the overall Dolomities territory
'Nova Levante' contains 6.319 km² of Dolomities.and the 0.4454% of the overall Dolomities territory
'Nova Ponente' contains 4.281 km² of Dolomities.and the 0.3018% of the overall Dolomities territory
'Ortisei' contains 0.317 km² of Dolomities.and the 0.0224% of the overall Dolomities territory
'San Candido' contains 28.486 km² of Dolomities.and the 2.0078% of the overall Dolomities territory
'San Martino in Badia' contains 18.621 km² of Dolomities.and the 1.3125% of the overall Dolomities territory
'Santa Cristina Valgardena' contains 8.183 km² of Dolomities.and the 0.5768% of the overall Dolomities territory
'Selva di Val Gardena' contains 22.321 km² of Dolomities.and the 1.5732% of the overall Dolomities territory
'Sesto' contains 29.706 km² of Dolomities.and the 2.0937% of the overall Dolomities territory
'Tires' contains 22.645 km² of Dolomities.and the 1.596% of the overall Dolomities territory
'Valdaora' contains 6.246 km² of Dolomities.and the 0.4403% of the overall Dolomities territory
'La Valle' contains 7.848 km² of Dolomities.and the 0.5531% of the overall Dolomities territory

df_mun_BZ = pd.DataFrame(list(zip(list_mun_BZ, list_area_BZ, list_percentage_BZ)),
               columns =['municipality', 'Dolomiti_area', 'percentage_over_total_Dolimiti_area'])

sorted_df_BZ = df_mun_BZ.sort_values(by=['Dolomiti_area'], ascending=False)
sorted_df_BZ.head(5)

	municipality	Dolomiti_area	percentage_over_total_Dolimiti_area
8	Marebbe	1.021015e+08	7.196300
5	Dobbiaco	5.991817e+07	4.223144
2	Braies	5.477485e+07	3.860633
16	Sesto	2.970603e+07	2.093736
12	San Candido	2.848610e+07	2.007753

sorted_df_BZ["province"] = 'Bolzano'

Some municipalities have quite high percentages being only individual municipalities.

Let's add the municipalities to the overall municipalities rankig list:

dolomiti_municipality_rank = sorted_df.append(pd.DataFrame(data = sorted_df_BZ), ignore_index=True)
dolomiti_municipality_rank = dolomiti_municipality_rank.sort_values(by=['Dolomiti_area'], ascending=False)
dolomiti_municipality_rank.head(5)

	municipality	Dolomiti_area	percentage_over_total_Dolimiti_area	province
22	Marebbe	1.021015e+08	7.196300	Bolzano
23	Dobbiaco	5.991817e+07	4.223144	Bolzano
24	Braies	5.477485e+07	3.860633	Bolzano
0	Primiero San Martino di Castrozza	4.132196e+07	2.912448	Trento
1	San Lorenzo Dorsino	3.670043e+07	2.586715	Trento

The podium is now entirely composed by municipalities in the province of Bolzano.

Which is the municipality with the highest area of Domolimiti in the province of Bolzano?

highest_dolomiti_area_mun = dolomiti_municipality_rank[dolomiti_municipality_rank.Dolomiti_area == dolomiti_municipality_rank.Dolomiti_area.max()]
marebbe = municipalities[municipalities.COMUNE == highest_dolomiti_area_mun.municipality.values[0]]
marebbe

	COD_REG	COD_PROV	PRO_COM_T	COMUNE	Shape_Area	geometry
2801	4	21	021047	Marebbe	1.589979e+08	POLYGON ((11.94437 46.74552, 11.95893 46.73875...

base = municipalities_BZ.plot(
    column='COD_PROV',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.5)

ctx.add_basemap(base, crs=municipalities_BZ.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)

marebbe.to_crs(epsg=4326).plot(
    figsize=(15,15),
    color="orange",
    edgecolor="k",
    lw=0.6,
    alpha=0.9,
    ax=base
    )

dolomities_BZ_clipped.to_crs(epsg=4326).plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.75,
    lw=0.1,
    )

<AxesSubplot:>

Marebbe is the municipality with the highest area of Domolimiti in the province of Bolzano. Let's check if areas and percentage of the municipalities sum up to that of the province:

round(dolomiti_BZ_area, -2) == round(sum(sorted_df_BZ.Dolomiti_area), -2)

True

round(persentage_dolomiti_BZ, -2) == round(sum(sorted_df_BZ.percentage_over_total_Dolimiti_area), -2)

True

Province of Belluno

Province of Belluno is the only province in Veneto that contains portion of Dolomiti's territory. Nevertheless, this province makes Veneto the second region for Dolomite area.

province_BL = provinces[provinces.DEN_PROV == 'Belluno']
province_BL

	COD_REG	COD_PROV	DEN_PROV	Shape_Area	geometry
24	5	25	Belluno	3.609841e+09	POLYGON ((12.50591 46.67839, 12.50603 46.67803...

province_BL.plot()

<AxesSubplot:>

Extract Dolomiti systems of province of Belluno

dolomities_BL = geodf_dolomities_provices[geodf_dolomities_provices.DEN_PROV == 'Belluno']
dolomities_BL

	Name	Description	geometry	area	COD_REG_	COD_PROV	DEN_PROV	Shape_Area_	DEN_REG	Shape_Area_reg
0	Sistema 1 - Pelmo, Croda da Lago	Esplora il Sistema 1 Pelmo, Croda da Lago su<b...	POLYGON Z ((12.12142 46.40488 0.00000, 12.1214...	4.315303e+07	5	25	Belluno	3.609841e+09	Veneto	1.834355e+10
1	Sistema 2 - Marmolada	Esplora il Sistema 2 Marmolada su<br>http://ww...	POLYGON Z ((11.82319 46.45296 0.00000, 11.8221...	2.205786e+07	5	25	Belluno	3.609841e+09	Veneto	1.834355e+10
2	Sistema 3 - Pale di San Martino, San Lucano, D...	Esplora il Sistema 3 Pale di San Martino, San ...	POLYGON Z ((11.98683 46.31720 0.00000, 11.9880...	3.165677e+08	5	25	Belluno	3.609841e+09	Veneto	1.834355e+10
3	Sistema 4 - Dolomiti friulane e d'Oltre Piave	Esplora il Sistema 4 Dolomiti friulane e d'Olt...	POLYGON Z ((12.51205 46.42487 0.00000, 12.5120...	2.146157e+08	5	25	Belluno	3.609841e+09	Veneto	1.834355e+10
4	Sistema 5 - Dolomiti settentrionali	Esplora il Sistema 5 Dolomiti settentrionali s...	MULTIPOLYGON Z (((12.16274 46.57915 0.00000, 1...	5.360157e+08	5	25	Belluno	3.609841e+09	Veneto	1.834355e+10

print(province_BL.DEN_PROV.values[0], ' contains ', len(dolomities_BL.Name.unique()), " Dolomiti systems:", sep="")
for i in dolomities_BL.Name.unique():
    print("-", i, end="\n")

Belluno contains 5 Dolomiti systems:
- Sistema 1 - Pelmo, Croda da Lago
- Sistema 2 - Marmolada
- Sistema 3 - Pale di San Martino, San Lucano, Dolomiti Bellunesi, Vette Feltrine
- Sistema 4 - Dolomiti friulane e d'Oltre Piave
- Sistema 5 - Dolomiti settentrionali

base = province_BL.to_crs(epsg=4326).plot(
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7
    )
ctx.add_basemap(base, zoom=12, crs=province_BL.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


dolomities_BL.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.70,
    lw=0.1,
    )

<AxesSubplot:>

Clip the Dolomiti systems geometry in order to have only the territory inside the province boudaries

dolomities_BL_clipped = dolomities_BL.clip(province_BL)

base = province_BL.to_crs(epsg=4326).plot(
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7
    )
ctx.add_basemap(base, zoom=12, crs=province_BL.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


dolomities_BL_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.70,
    lw=0.1,
    )

<AxesSubplot:>

Recalculate clipped Dolomiti's territory area

dolomities_BL_clipped_areas= dolomities_BL_clipped.to_crs(epsg=32632)
dolomities_BL_clipped_areas["area"] = dolomities_BL_clipped_areas['geometry'].area

Sum the Dolomiti's areas in province of Belluno:

dolomiti_BL_area = sum(dolomities_BL_clipped_areas.area)
print(round(dolomiti_BL_area/10**6, 2), " km\u00b2 of Dolomiti are in province of Trento", sep="")

584.6 km² of Dolomiti are in province of Trento

dolomiti_BL_overlay = province_BL.to_crs(epsg=32632).overlay(dolomities_BL_clipped.to_crs(epsg=32632), how="difference")
base = province_BL.to_crs(epsg=4326).plot(
    figsize=(15,15),
    color= "white",
    edgecolor="k",
    lw=0.7,
    alpha=0.7
    )
dolomiti_BL_overlay.to_crs(epsg=4326).plot(ax=base)

<AxesSubplot:>

We can see much more impact on the province territory with respect to the previous provinces.

Proportion of Dolomiti's territory in province of Belluno

dolomiti_BL_area = sum(dolomities_BL_clipped_areas.area)
overal_dolomiti_area = sum(geodf_dolomities['area'])

persentage_dolomiti_BL = (dolomiti_BL_area/overal_dolomiti_area)*100

print("Province of Belluno contains the ", round(persentage_dolomiti_BL, 2), "% of the Dolomiti's terriotory", sep="")

Province of Belluno contains the 41.2% of the Dolomiti's terriotory

The province of Belluno contains 10% more Dolomiti's territory than Bolzano.

What proportion of each system does the province contain?

for index, row in dolomities_BL.iterrows():
    province_systems_percentage(row.DEN_PROV, row.Name)

100.0% of the 'Sistema 1 - Pelmo, Croda da Lago' is in province of Belluno
47.92% of the 'Sistema 2 - Marmolada' is in province of Belluno
85.53% of the 'Sistema 3 - Pale di San Martino, San Lucano, Dolomiti Bellunesi, Vette Feltrine' is in province of Belluno
12.71% of the 'Sistema 4 - Dolomiti friulane e d'Oltre Piave' is in province of Belluno
43.44% of the 'Sistema 5 - Dolomiti settentrionali' is in province of Belluno

Province of Belluno contain the entire Sistema 1, higher percentages of Sistema 3, half of Sistema 2 and portions of Sistemna 5 and Sistema 4.

Dolomiti's municipalities of province of Belluno

code_BL = province_BL.COD_PROV.values[0]
code_BL

25

municipalities_BL = municipalities[municipalities.COD_PROV == code_BL]
municipalities_BL.head(4)

	COD_REG	COD_PROV	PRO_COM_T	COMUNE	Shape_Area	geometry
3249	5	25	025001	Agordo	2.411324e+07	POLYGON ((12.06082 46.33488, 12.07344 46.33107...
3250	5	25	025002	Alano di Piave	3.648229e+07	POLYGON ((11.89820 45.93666, 11.90215 45.93687...
3251	5	25	025003	Alleghe	2.956926e+07	POLYGON ((11.99511 46.44236, 12.00606 46.43859...
3252	5	25	025004	Arsiè	6.469551e+07	POLYGON ((11.73171 46.01990, 11.73636 46.02037...

municipalities_BL.plot()

<AxesSubplot:>

Get municipalities that contain Dolomiti's territory

geo_dolomiti_BL_mun = municipalities_BL[municipalities_BL.COMUNE.isin(dolomiti_mun)]

print("Municipalities in province of Trento that contain Dolomiti: ")
for i in geo_dolomiti_BL_mun.COMUNE.unique():
    print("-", i, end="\n")

Municipalities in province of Trento that contain Dolomiti: 
- Agordo
- Alleghe
- Auronzo di Cadore
- Belluno
- Borca di Cadore
- Calalzo di Cadore
- Cencenighe Agordino
- Cesiomaggiore
- Colle Santa Lucia
- Comelico Superiore
- Cortina d'Ampezzo
- Domegge di Cadore
- Falcade
- Feltre
- Canale d'Agordo
- Gosaldo
- La Valle Agordina
- Livinallongo del Col di Lana
- Lorenzago di Cadore
- Lozzo di Cadore
- Ospitale di Cadore
- Perarolo di Cadore
- Pieve di Cadore
- Rivamonte Agordino
- Rocca Pietore
- San Gregorio nelle Alpi
- Santa Giustina
- San Tomaso Agordino
- San Vito di Cadore
- Sedico
- Selva di Cadore
- Sospirolo
- Sovramonte
- Taibon Agordino
- Vodo Cadore
- Voltago Agordino
- Zoppè di Cadore
- Longarone
- Val di Zoldo

We have a lot of involed municipalities here.

base = municipalities_BL.plot(
    column='COD_PROV',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="#413A3B",
    lw=0.6,
    alpha=0.5)

ctx.add_basemap(base, crs=municipalities_BL.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)

geo_dolomiti_BL_mun.to_crs(epsg=4326).plot(
    column='COMUNE',
    figsize=(15,15),
    cmap="Paired",
    edgecolor="k",
    lw=0.2,
    alpha=0.4,
    ax=base
    )

dolomities_BL_clipped.to_crs(epsg=4326).plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.85,
    lw=1,
    )

<AxesSubplot:>

Municipalities that include Dolomiti's territory also appear to be the majority of municipalities in the province of Belluno.

Which is the municiplaity that contains the highest area of Dolomiti?

list_mun_BL = []
list_area_BL = []
list_percentage_BL = []

for index, row in geo_dolomiti_BL_mun.iterrows():
    temp = mun_systems_area(row.COMUNE)

    list_mun_BL.append(temp[0])
    list_area_BL.append(temp[1])
    list_percentage_BL.append(temp[2])

'Agordo' contains 2.783 km² of Dolomities.and the 0.1962% of the overall Dolomities territory
'Alleghe' contains 3.146 km² of Dolomities.and the 0.2217% of the overall Dolomities territory
'Auronzo di Cadore' contains 79.371 km² of Dolomities.and the 5.5942% of the overall Dolomities territory
'Belluno' contains 2.71 km² of Dolomities.and the 0.191% of the overall Dolomities territory
'Borca di Cadore' contains 9.347 km² of Dolomities.and the 0.6588% of the overall Dolomities territory
'Calalzo di Cadore' contains 18.463 km² of Dolomities.and the 1.3013% of the overall Dolomities territory
'Cencenighe Agordino' contains 1.683 km² of Dolomities.and the 0.1186% of the overall Dolomities territory
'Cesiomaggiore' contains 26.679 km² of Dolomities.and the 1.8804% of the overall Dolomities territory
'Colle Santa Lucia' contains 0.008 km² of Dolomities.and the 0.0006% of the overall Dolomities territory
'Comelico Superiore' contains 6.373 km² of Dolomities.and the 0.4492% of the overall Dolomities territory
'Cortina d'Ampezzo' contains 105.371 km² of Dolomities.and the 7.4267% of the overall Dolomities territory
'Domegge di Cadore' contains 8.596 km² of Dolomities.and the 0.6059% of the overall Dolomities territory
'Falcade' contains 3.908 km² of Dolomities.and the 0.2755% of the overall Dolomities territory
'Feltre' contains 8.203 km² of Dolomities.and the 0.5781% of the overall Dolomities territory
'Canale d'Agordo' contains 16.696 km² of Dolomities.and the 1.1767% of the overall Dolomities territory
'Gosaldo' contains 10.204 km² of Dolomities.and the 0.7192% of the overall Dolomities territory
'La Valle Agordina' contains 8.377 km² of Dolomities.and the 0.5904% of the overall Dolomities territory
'Livinallongo del Col di Lana' contains 3.298 km² of Dolomities.and the 0.2324% of the overall Dolomities territory
'Lorenzago di Cadore' contains 4.859 km² of Dolomities.and the 0.3425% of the overall Dolomities territory
'Lozzo di Cadore' contains 0.999 km² of Dolomities.and the 0.0704% of the overall Dolomities territory
'Ospitale di Cadore' contains 0.011 km² of Dolomities.and the 0.0008% of the overall Dolomities territory
'Perarolo di Cadore' contains 5.339 km² of Dolomities.and the 0.3763% of the overall Dolomities territory
'Pieve di Cadore' contains 14.489 km² of Dolomities.and the 1.0212% of the overall Dolomities territory
'Rivamonte Agordino' contains 4.392 km² of Dolomities.and the 0.3096% of the overall Dolomities territory
'Rocca Pietore' contains 10.261 km² of Dolomities.and the 0.7232% of the overall Dolomities territory
'San Gregorio nelle Alpi' contains 2.354 km² of Dolomities.and the 0.1659% of the overall Dolomities territory
'Santa Giustina' contains 1.036 km² of Dolomities.and the 0.073% of the overall Dolomities territory
'San Tomaso Agordino' contains 0.091 km² of Dolomities.and the 0.0064% of the overall Dolomities territory
'San Vito di Cadore' contains 23.139 km² of Dolomities.and the 1.6309% of the overall Dolomities territory
'Sedico' contains 42.879 km² of Dolomities.and the 3.0222% of the overall Dolomities territory
'Selva di Cadore' contains 7.736 km² of Dolomities.and the 0.5453% of the overall Dolomities territory
'Sospirolo' contains 20.799 km² of Dolomities.and the 1.466% of the overall Dolomities territory
'Sovramonte' contains 10.52 km² of Dolomities.and the 0.7415% of the overall Dolomities territory
'Taibon Agordino' contains 61.985 km² of Dolomities.and the 4.3688% of the overall Dolomities territory
'Vodo Cadore' contains 11.578 km² of Dolomities.and the 0.816% of the overall Dolomities territory
'Voltago Agordino' contains 2.968 km² of Dolomities.and the 0.2092% of the overall Dolomities territory
'Zoppè di Cadore' contains 0.736 km² of Dolomities.and the 0.0519% of the overall Dolomities territory
'Longarone' contains 16.679 km² of Dolomities.and the 1.1755% of the overall Dolomities territory
'Val di Zoldo' contains 26.531 km² of Dolomities.and the 1.8699% of the overall Dolomities territory

df_mun_BL = pd.DataFrame(list(zip(list_mun_BL, list_area_BL, list_percentage_BL)),
               columns =['municipality', 'Dolomiti_area', 'percentage_over_total_Dolimiti_area'])

sorted_df_BL = df_mun_BL.sort_values(by=['Dolomiti_area'], ascending=False)
sorted_df_BL.head(5)

	municipality	Dolomiti_area	percentage_over_total_Dolimiti_area
10	Cortina d'Ampezzo	1.053706e+08	7.426714
2	Auronzo di Cadore	7.937064e+07	5.594190
33	Taibon Agordino	6.198461e+07	4.368790
29	Sedico	4.287910e+07	3.022198
7	Cesiomaggiore	2.667912e+07	1.880394

As in the province of Bolzano, we still have high percentages for individual municipalities.

Which is the municipality with the highest area of Domolomiti in the province of Belluno?

highest_dolomiti_area_mun = sorted_df_BL[sorted_df_BL.Dolomiti_area == sorted_df_BL.Dolomiti_area.max()]
cortina = municipalities[municipalities.COMUNE == highest_dolomiti_area_mun.municipality.values[0]]
cortina

	COD_REG	COD_PROV	PRO_COM_T	COMUNE	Shape_Area	geometry
3263	5	25	025016	Cortina d'Ampezzo	2.534631e+08	POLYGON ((12.07025 46.67491, 12.07473 46.67458...

base = municipalities_BL.plot(
    column='COD_PROV',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.5)

ctx.add_basemap(base, crs=municipalities_BL.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)

cortina.to_crs(epsg=4326).plot(
    figsize=(15,15),
    color="orange",
    edgecolor="k",
    lw=0.6,
    alpha=0.9,
    ax=base
    )

dolomities_BL_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.75,
    lw=0.1,
    )

<AxesSubplot:>

Cortina d'Ampezzo is the municipality with the highest area of Domolomiti in the province of Belluno. Let's check if areas and percentage of the municipalities sum up to that of the province:

round(dolomiti_BL_area, -2) == round(sum(sorted_df_BL.Dolomiti_area), -2)

True

round(persentage_dolomiti_BL, -2) == round(sum(sorted_df_BL.percentage_over_total_Dolimiti_area), -2)

True

Is Cortina d'Ampezzo also highest than Marebbe and the other municipalities? Let's reformulate the ranking list.

sorted_df_BL["province"] = 'Belluno'

dolomiti_municipality_rank = dolomiti_municipality_rank.append(pd.DataFrame(data = sorted_df_BL), ignore_index=True)
dolomiti_municipality_rank = dolomiti_municipality_rank.sort_values(by=['Dolomiti_area'], ascending=False)
dolomiti_municipality_rank.head(5)

	municipality	Dolomiti_area	percentage_over_total_Dolimiti_area	province
42	Cortina d'Ampezzo	1.053706e+08	7.426714	Belluno
0	Marebbe	1.021015e+08	7.196300	Bolzano
43	Auronzo di Cadore	7.937064e+07	5.594190	Belluno
44	Taibon Agordino	6.198461e+07	4.368790	Belluno
1	Dobbiaco	5.991817e+07	4.223144	Bolzano

Yes, it is. Now the podium is mainly composed of municipalities in the province of Bellluno.

Province of Pordenone

Province of Pordenone is in Friuli Venezia Giulia, and together with province of Udine made this region the last Dolomiti's region.

province_PN = provinces[provinces.DEN_PROV == 'Pordenone']
province_PN

	COD_REG	COD_PROV	DEN_PROV	Shape_Area	geometry
92	6	93	Pordenone	2.275459e+09	POLYGON ((12.52110 46.38899, 12.52360 46.38049...

province_PN.plot()

<AxesSubplot:>

Extract Dolomiti systems of province of Pordenone:

dolomities_PN = geodf_dolomities_provices[geodf_dolomities_provices.DEN_PROV == 'Pordenone']
dolomities_PN

	Name	Description	geometry	area	COD_REG_	COD_PROV	DEN_PROV	Shape_Area_	DEN_REG	Shape_Area_reg
3	Sistema 4 - Dolomiti friulane e d'Oltre Piave	Esplora il Sistema 4 Dolomiti friulane e d'Olt...	POLYGON Z ((12.51205 46.42487 0.00000, 12.5120...	2.146157e+08	6	93	Pordenone	2.275459e+09	Friuli Venezia Giulia	7.933911e+09

print(province_PN.DEN_PROV.values[0], ' contains ', len(dolomities_PN.Name.unique()), " Dolomiti systems:", sep="")
for i in dolomities_PN.Name.unique():
    print("-", i, end="\n")

Pordenone contains 1 Dolomiti systems:
- Sistema 4 - Dolomiti friulane e d'Oltre Piave

base = province_PN.to_crs(epsg=4326).plot(
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7
    )
ctx.add_basemap(base, zoom=12, crs=province_PN.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


dolomities_PN.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.70,
    lw=0.1,
    )

<AxesSubplot:>

As we seen the only system in Friuli Venezia Giulia is Sistema 4 - Dolomiti friulane e d'Oltre Piave

Clip the Dolomiti systems geometry in order to have only the territory inside the province boudaries

dolomities_PN_clipped = dolomities_PN.clip(province_PN)

base = province_PN.to_crs(epsg=4326).plot(
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7
    )
ctx.add_basemap(base, zoom=12, crs=province_PN.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


dolomities_PN_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.70,
    lw=0.1,
    )

<AxesSubplot:>

Recalculate clipped Dolomiti's territory area

dolomities_PN_clipped_areas = dolomities_PN_clipped.to_crs(epsg=32632)
dolomities_PN_clipped_areas["area"] = dolomities_PN_clipped_areas['geometry'].area
dolomities_PN_clipped_areas

	Name	Description	geometry	area	COD_REG_	COD_PROV	DEN_PROV	Shape_Area_	DEN_REG	Shape_Area_reg
3	Sistema 4 - Dolomiti friulane e d'Oltre Piave	Esplora il Sistema 4 Dolomiti friulane e d'Olt...	POLYGON ((762929.905 5139138.528, 762933.346 5...	1.519279e+08	6	93	Pordenone	2.275459e+09	Friuli Venezia Giulia	7.933911e+09

Sum the Dolomiti's areas in province of Pordenone:

dolomiti_PN_area = sum(dolomities_PN_clipped_areas.area)
print(round(dolomiti_PN_area/10**6, 2), " km\u00b2 of Dolomiti are in province of Pordenone", sep="")

151.93 km² of Dolomiti are in province of Pordenone

Since Friuli Venezia Giulia contains a low percentage of Dolomiti's territory, also province of Pordenone contains only a small area.

Proportion of Dolomiti's territory in province of Pordenone

dolomiti_PN_area = sum(dolomities_PN_clipped_areas.area)
overal_dolomiti_area = sum(geodf_dolomities['area'])

persentage_dolomiti_PN = (dolomiti_PN_area/overal_dolomiti_area)*100

print("Province of Trento contains the ", round(persentage_dolomiti_PN, 2), "% of the Dolomiti's terriotory", sep="")

Province of Trento contains the 10.71% of the Dolomiti's terriotory

the province contains 4% fewer territories than the province of Trento (14%).

Proportion of Dolomiti's territory in province of Pordenone

for index, row in dolomities_PN.iterrows():
    province_systems_percentage(row.DEN_PROV, row.Name)

70.79% of the 'Sistema 4 - Dolomiti friulane e d'Oltre Piave' is in province of Pordenone

By the way province of Pordenone contains the majority of Sistema 4

Dolomiti's municipalities of province of Pordenone

Get municipalities of province of Pordenone

code_PN = province_PN.COD_PROV.values[0]
code_PN

93

municipalities_PN = municipalities[municipalities.COD_PROV == code_PN]
municipalities_PN.head(4)

	COD_REG	COD_PROV	PRO_COM_T	COMUNE	Shape_Area	geometry
7101	6	93	093001	Andreis	2.732638e+07	POLYGON ((12.60761 46.24313, 12.61041 46.24260...
7102	6	93	093002	Arba	1.535937e+07	POLYGON ((12.80817 46.17472, 12.80894 46.17257...
7103	6	93	093004	Aviano	1.137879e+08	POLYGON ((12.57556 46.03119, 12.57536 46.03177...
7104	6	93	093005	Azzano Decimo	5.115717e+07	POLYGON ((12.68656 45.92821, 12.68735 45.92826...

municipalities_PN.plot()

<AxesSubplot:>

Get municipalities that contain Dolomiti's territory

geo_dolomiti_PN_mun = municipalities_PN[municipalities_PN.COMUNE.isin(dolomiti_mun)]

print("Municipalities in province of Pordenone that contain Dolomiti: ")
for i in geo_dolomiti_PN_mun.COMUNE.unique():
    print("-", i, end="\n")

Municipalities in province of Pordenone that contain Dolomiti: 
- Cimolais
- Claut
- Erto e Casso
- Tramonti di Sopra

As expected only few municipalities contains Dolomiti's peaks.

base = municipalities_PN.plot(
    column='COD_PROV',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.6)

ctx.add_basemap(base, crs=municipalities_PN.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)

geo_dolomiti_PN_mun.to_crs(epsg=4326).plot(
    column='COMUNE',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7,
    ax=base
    )

dolomities_PN_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.80,
    lw=0.8,
    )

<AxesSubplot:>

Which is the municiplaity that contains the highest area of Dolomiti?

list_mun_PN = []
list_area_PN = []
list_percentage_PN = []

for index, row in geo_dolomiti_PN_mun.iterrows():
    temp = mun_systems_area(row.COMUNE)

    list_mun_PN.append(temp[0])
    list_area_PN.append(temp[1])
    list_percentage_PN.append(temp[2])

'Cimolais' contains 70.795 km² of Dolomities.and the 4.9898% of the overall Dolomities territory
'Claut' contains 63.226 km² of Dolomities.and the 4.4563% of the overall Dolomities territory
'Erto e Casso' contains 14.635 km² of Dolomities.and the 1.0315% of the overall Dolomities territory
'Tramonti di Sopra' contains 3.272 km² of Dolomities.and the 0.2306% of the overall Dolomities territory

df_mun_PN = pd.DataFrame(list(zip(list_mun_PN, list_area_PN, list_percentage_PN)),
               columns =['municipality', 'Dolomiti_area', 'percentage_over_total_Dolimiti_area'])

sorted_df_PN = df_mun_PN.sort_values(by=['Dolomiti_area'], ascending=False)
sorted_df_PN

	municipality	Dolomiti_area	percentage_over_total_Dolimiti_area
0	Cimolais	7.079494e+07	4.989759
1	Claut	6.322577e+07	4.456270
2	Erto e Casso	1.463481e+07	1.031488
3	Tramonti di Sopra	3.272357e+06	0.230642

Which is the municipality with the highest area of Domolomiti in the province of Pordenone?

highest_dolomiti_area_mun_PN = sorted_df_PN[sorted_df_PN.Dolomiti_area == sorted_df_PN.Dolomiti_area.max()]
cimolais = municipalities[municipalities.COMUNE == highest_dolomiti_area_mun_PN.municipality.values[0]]
cimolais

	COD_REG	COD_PROV	PRO_COM_T	COMUNE	Shape_Area	geometry
7113	6	93	093014	Cimolais	100363518.1	POLYGON ((12.50473 46.40423, 12.50815 46.40305...

base = municipalities_PN.plot(
    column='COD_PROV',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.5)

ctx.add_basemap(base, crs=municipalities_PN.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)

cimolais.to_crs(epsg=4326).plot(
    figsize=(15,15),
    color="orange",
    edgecolor="k",
    lw=0.6,
    alpha=0.9,
    ax=base
    )

dolomities_PN_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.75,
    lw=0.1,
    )

<AxesSubplot:>

Cimolais is the municipality with the highest area of Domolomiti in the province of Pordenone.

Let's check if areas and percentage of the municipalities sum up to that of the province of Trento:

round(dolomiti_PN_area, -2) == round(sum(sorted_df_PN.Dolomiti_area), -2)

True

round(persentage_dolomiti_PN, -2) == round(sum(sorted_df_PN.percentage_over_total_Dolimiti_area), -2)

True

Add Pordenone municipalities to the other Dolomiti's municipalities:

sorted_df_PN["province"] = 'Pordenone'
dolomiti_municipality_rank = dolomiti_municipality_rank.append(pd.DataFrame(data = sorted_df_PN), ignore_index=True)
dolomiti_municipality_rank = dolomiti_municipality_rank.sort_values(by=['Dolomiti_area'], ascending=False)
dolomiti_municipality_rank

	municipality	Dolomiti_area	percentage_over_total_Dolimiti_area	province
0	Cortina d'Ampezzo	1.053706e+08	7.426714	Belluno
1	Marebbe	1.021015e+08	7.196300	Bolzano
2	Auronzo di Cadore	7.937064e+07	5.594190	Belluno
81	Cimolais	7.079494e+07	4.989759	Pordenone
82	Claut	6.322577e+07	4.456270	Pordenone
...	...	...	...	...
76	Imer	1.894005e+05	0.013349	Trento
77	San Tomaso Agordino	9.145406e+04	0.006446	Belluno
78	Andalo	3.618142e+04	0.002550	Trento
79	Ospitale di Cadore	1.114393e+04	0.000785	Belluno
80	Colle Santa Lucia	8.043294e+03	0.000567	Belluno

85 rows × 4 columns

Municipalities of province od Pordenone cannot compete with those of the provinces of Bolzano and Belluno. Nevertheless, Cimolais and Claut are fourth and fifth in the global ranking.

Province of Udine

The province of Udine is the other Dolomiti's province of Friuli Venezia Giulia.

province_UD = provinces[provinces.DEN_PROV == 'Udine']
province_UD

	COD_REG	COD_PROV	DEN_PROV	Shape_Area	geometry
29	6	30	Udine	4.969771e+09	POLYGON ((12.76335 46.64757, 12.76549 46.64669...

province_UD.plot()

<AxesSubplot:>

Extract Dolomiti systems of province of Udine:

dolomities_UD = geodf_dolomities_provices[geodf_dolomities_provices.DEN_PROV == 'Udine']
dolomities_UD

	Name	Description	geometry	area	COD_REG_	COD_PROV	DEN_PROV	Shape_Area_	DEN_REG	Shape_Area_reg
3	Sistema 4 - Dolomiti friulane e d'Oltre Piave	Esplora il Sistema 4 Dolomiti friulane e d'Olt...	POLYGON Z ((12.51205 46.42487 0.00000, 12.5120...	2.146157e+08	6	30	Udine	4.969771e+09	Friuli Venezia Giulia	7.933911e+09

As for the other province of Friuli Venezia Giulia, the province of Udine contains only Sistema 4 - Dolomiti friulane e d'Oltre Piave.

base = province_UD.to_crs(epsg=4326).plot(
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7
    )
ctx.add_basemap(base, zoom=12, crs=province_UD.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


dolomities_UD.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.70,
    lw=0.1,
    )

<AxesSubplot:>

Clip the Dolomiti systems geometry in order to have only the territory inside the province boudaries

dolomities_UD_clipped = dolomities_UD.clip(province_UD)

base = province_UD.to_crs(epsg=4326).plot(
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.7
    )
ctx.add_basemap(base, zoom=12, crs=province_UD.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)


dolomities_UD_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.70,
    lw=0.1,
    )

<AxesSubplot:>

Recalculate clipped Dolomiti's territory area

dolomities_UD_clipped_areas= dolomities_UD_clipped.to_crs(epsg=32632)
dolomities_UD_clipped_areas["area"] = dolomities_UD_clipped_areas['geometry'].area/10**6 # for km^2
dolomities_UD_clipped_areas

	Name	Description	geometry	area	COD_REG_	COD_PROV	DEN_PROV	Shape_Area_	DEN_REG	Shape_Area_reg
3	Sistema 4 - Dolomiti friulane e d'Oltre Piave	Esplora il Sistema 4 Dolomiti friulane e d'Olt...	POLYGON ((769649.774 5149335.876, 769631.633 5...	35.419384	6	30	Udine	4.969771e+09	Friuli Venezia Giulia	7.933911e+09

Sum the Dolomiti's areas in province of Udine:

dolomiti_UD_area = sum(dolomities_UD_clipped_areas.area)
print(round(dolomiti_UD_area/10**6, 2), " km\u00b2 of Dolomiti are in province of Udine", sep="")

35.42 km² of Dolomiti are in province of Udine

As it is possible to see also from the previous plots, province of Udine contains a very small Dolomiti's area.

Proportion of Dolomiti's territory in province of Udine

dolomiti_UD_area = sum(dolomities_UD_clipped_areas.area)
overal_dolomiti_area = sum(geodf_dolomities['area'])

persentage_dolomiti_UD = (dolomiti_UD_area/overal_dolomiti_area)*100

print("Province of Udine contains the ", round(persentage_dolomiti_UD, 2), "% of the Dolomiti's terriotory", sep="")

Province of Udine contains the 2.5% of the Dolomiti's terriotory

As expected, also the percentage is very low. And this made it the last province for the amount of Dolomiti's territory.

What proportion of each system does the province contain?

for index, row in dolomities_UD.iterrows():
    province_systems_percentage(row.DEN_PROV, row.Name)

16.5% of the 'Sistema 4 - Dolomiti friulane e d'Oltre Piave' is in province of Udine

Province of Udine contains only a small parte of Sistema 4.

Dolomiti's municipalities of province of Udine

Get municipalities of province of Udine

code_UD = province_UD.COD_PROV.values[0]
code_UD

30

municipalities_UD = municipalities[municipalities.COD_PROV == code_UD]
municipalities_UD.head(4)

	COD_REG	COD_PROV	PRO_COM_T	COMUNE	Shape_Area	geometry
3600	6	30	030001	Aiello del Friuli	1.295954e+07	POLYGON ((13.37162 45.88693, 13.37192 45.88570...
3601	6	30	030002	Amaro	3.330734e+07	POLYGON ((13.10669 46.41845, 13.11435 46.41627...
3602	6	30	030003	Ampezzo	7.330908e+07	POLYGON ((12.77522 46.47898, 12.77537 46.47816...
3603	6	30	030004	Aquileia	3.730129e+07	POLYGON ((13.39107 45.79862, 13.39172 45.79726...

municipalities_UD.plot()

<AxesSubplot:>

Get municipalities that contain Dolomiti's territory

geo_dolomiti_UD_mun = municipalities_UD[municipalities_UD.COMUNE.isin(dolomiti_mun)]

print("Municipalities in province of Udine that contain Dolomiti: ")
for i in geo_dolomiti_UD_mun.COMUNE.unique():
    print("-", i, end="\n")

Municipalities in province of Udine that contain Dolomiti: 
- Forni di Sopra
- Forni di Sotto

Only two municipalities are involved here.

base = municipalities_UD.plot(
    column='COD_PROV',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="#413A3B",
    lw=0.6,
    alpha=0.5)

ctx.add_basemap(base, crs=municipalities_UD.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)

geo_dolomiti_UD_mun.to_crs(epsg=4326).plot(
    column='COMUNE',
    figsize=(15,15),
    cmap="Paired",
    edgecolor="k",
    lw=0.2,
    alpha=0.7,
    ax=base
    )

dolomities_UD_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.85,
    lw=1,
    )

<AxesSubplot:>

Which is the municiplaity that contains the highest area of Dolomiti?

list_mun_UD = []
list_area_UD = []
list_percentage_UD = []

for index, row in geo_dolomiti_UD_mun.iterrows():
    temp = mun_systems_area(row.COMUNE)

    list_mun_UD.append(temp[0])
    list_area_UD.append(temp[1])
    list_percentage_UD.append(temp[2])

'Forni di Sopra' contains 24.53 km² of Dolomities.and the 1.7289% of the overall Dolomities territory
'Forni di Sotto' contains 10.889 km² of Dolomities.and the 0.7675% of the overall Dolomities territory

Of course also here the percentage are very low.

df_mun_UD = pd.DataFrame(list(zip(list_mun_UD, list_area_UD, list_percentage_UD)),
               columns =['municipality', 'Dolomiti_area', 'percentage_over_total_Dolimiti_area'])

sorted_df_UD = df_mun_UD.sort_values(by=['Dolomiti_area'], ascending=False)
sorted_df_UD

	municipality	Dolomiti_area	percentage_over_total_Dolimiti_area
0	Forni di Sopra	2.452997e+07	1.728918
1	Forni di Sotto	1.088941e+07	0.767506

Which is the municipality with the highest area of Domolomiti in the province of Udine?

highest_dolomiti_area_mun_UD = sorted_df_UD[sorted_df_UD.Dolomiti_area == sorted_df_UD.Dolomiti_area.max()]
forni_di_sopra = municipalities[municipalities.COMUNE == highest_dolomiti_area_mun_UD.municipality.values[0]]
forni_di_sopra

	COD_REG	COD_PROV	PRO_COM_T	COMUNE	Shape_Area	geometry
3638	6	30	030041	Forni di Sopra	8.214593e+07	POLYGON ((12.57608 46.47337, 12.57838 46.47358...

base = municipalities_UD.plot(
    column='COD_PROV',
    figsize=(15,15),
    cmap="Pastel1",
    edgecolor="k",
    lw=0.2,
    alpha=0.5)

ctx.add_basemap(base, crs=municipalities_UD.crs.to_string(), source=ctx.providers.Stamen.TerrainBackground, alpha=0.7)

forni_di_sopra.to_crs(epsg=4326).plot(
    figsize=(15,15),
    color="orange",
    edgecolor="k",
    lw=0.6,
    alpha=0.9,
    ax=base
    )

dolomities_UD_clipped.plot(
    column='Name',
    categorical=True,
    legend=True,
    ax=base,
    edgecolor="k",
    alpha=0.75,
    lw=0.1,
    )

<AxesSubplot:>

Forni di Sopra is the municipality with the highest area of Domolomiti in the province of Udine.

Let's check if areas and percentage of the municipalities sum up to that of the province of Udine:

round(dolomiti_UD_area, -2) == round(sum(sorted_df_UD.Dolomiti_area), -2)

True

round(persentage_dolomiti_UD, -2) == round(sum(sorted_df_UD.percentage_over_total_Dolimiti_area), -2)

True

Add the two municipalities to the other Dolomiti municipalities:

sorted_df_UD["province"] = 'Udine'
dolomiti_municipality_rank = dolomiti_municipality_rank.append(pd.DataFrame(data = sorted_df_UD), ignore_index=True)
dolomiti_municipality_rank = dolomiti_municipality_rank.sort_values(by=['Dolomiti_area'], ascending=False)
dolomiti_municipality_rank.head(5)

	municipality	Dolomiti_area	percentage_over_total_Dolimiti_area	province
0	Cortina d'Ampezzo	1.053706e+08	7.426714	Belluno
1	Marebbe	1.021015e+08	7.196300	Bolzano
2	Auronzo di Cadore	7.937064e+07	5.594190	Belluno
3	Cimolais	7.079494e+07	4.989759	Pordenone
4	Claut	6.322577e+07	4.456270	Pordenone

Clearly the podium remains unchanged. Let's see the position of procince of Udine municipalities:

dolomiti_municipality_rank[dolomiti_municipality_rank.province == 'Udine']

	municipality	Dolomiti_area	percentage_over_total_Dolimiti_area	province
85	Forni di Sopra	2.452997e+07	1.728918	Udine
86	Forni di Sotto	1.088941e+07	0.767506	Udine

They are 16th and 34th in the overal ranking list.

Final check:

Are the municipalities Dolomiti areas sum up to the whole Dolomiti area? (more or less 1418.80 km)

sum(dolomiti_municipality_rank.Dolomiti_area)/10**6

1418.8047759299952

Are the municipalities percentage_over_total_Dolomiti_area sum up to 100?

sum(dolomiti_municipality_rank.percentage_over_total_Dolimiti_area)

99.99999999999996

Summary: final ranking lists

Regions' ranking list

• 🥇 Trentino-AltoAdige
• 🥈 Veneto
• 🥉 Friuli Venezia Giulia

rank_dolomiti_reg

	region	dolomiti_area_km	percentage_over_total_dolimiti_area
0	Trentino-Alto Adige	646.86	45.59
1	Veneto	584.60	41.20
2	Friuli Venezia Giulia	187.35	13.20

Provinces' ranking list

• 🥇 Belluno (Veneto)
• 🥈 Bolzano (Trentino-Alto Adige)
• 🥉 Trento (Trentino-Alto Adige)
• 4° Pordenone (Friuli Venezia Giulia)
• 5° Udine (Friuli Venezia Giulia)

data = {'region': [province_TN.DEN_PROV.values[0], province_BZ.DEN_PROV.values[0], province_BL.DEN_PROV.values[0], province_PN.DEN_PROV.values[0], province_UD.DEN_PROV.values[0]],
        'dolomiti_area_km': [round(dolomiti_TN_area/10**6, 2), round(dolomiti_BZ_area/10**6, 2), round(dolomiti_BL_area/10**6, 2), round(dolomiti_PN_area/10**6, 2), round(dolomiti_UD_area/10**6, 2)],
        'percentage_over_total_dolimiti_area': [round(persentage_dolomiti_TN, 2), round(persentage_dolomiti_BZ, 2), round(persentage_dolomiti_BL, 2), round(persentage_dolomiti_PN, 2), round(persentage_dolomiti_UD, 2)]
        }

provinces_rank = pd.DataFrame(data)
provinces_rank = provinces_rank.sort_values(by=['dolomiti_area_km'], ascending=False)
provinces_rank

	region	dolomiti_area_km	percentage_over_total_dolimiti_area
2	Belluno	584.60	41.20
1	Bolzano	439.24	30.96
0	Trento	207.62	14.63
3	Pordenone	151.93	10.71
4	Udine	35.42	2.50

Municipalities' ranking list

• 🥇 Cortina d'Ampezzo (Belluno, Veneto)
• 🥈 Marebbe (Bolzano, Trentino-Alto Adige)
• 🥉 Auronzo di Cadore (Belluno, Veneto)

pd.set_option("display.max_rows", None, "display.max_columns", None)
dolomiti_municipality_rank

	municipality	Dolomiti_area	percentage_over_total_Dolimiti_area	province
0	Cortina d'Ampezzo	1.053706e+08	7.426714	Belluno
1	Marebbe	1.021015e+08	7.196300	Bolzano
2	Auronzo di Cadore	7.937064e+07	5.594190	Belluno
3	Cimolais	7.079494e+07	4.989759	Pordenone
4	Claut	6.322577e+07	4.456270	Pordenone
5	Taibon Agordino	6.198461e+07	4.368790	Belluno
6	Dobbiaco	5.991817e+07	4.223144	Bolzano
7	Braies	5.477485e+07	3.860633	Bolzano
8	Sedico	4.287910e+07	3.022198	Belluno
9	Primiero San Martino di Castrozza	4.132196e+07	2.912448	Trento
10	San Lorenzo Dorsino	3.670043e+07	2.586715	Trento
11	Sesto	2.970603e+07	2.093736	Bolzano
12	San Candido	2.848610e+07	2.007753	Bolzano
13	Cesiomaggiore	2.667912e+07	1.880394	Belluno
14	Val di Zoldo	2.653083e+07	1.869942	Belluno
15	San Giovanni di Fassa	2.481193e+07	1.748791	Trento
85	Forni di Sopra	2.452997e+07	1.728918	Udine
16	San Vito di Cadore	2.313881e+07	1.630866	Belluno
17	Badia	2.298394e+07	1.619951	Bolzano
18	Tires	2.264476e+07	1.596044	Bolzano
19	Selva di Val Gardena	2.232101e+07	1.573226	Bolzano
20	Sospirolo	2.079918e+07	1.465965	Belluno
21	San Martino in Badia	1.862120e+07	1.312457	Bolzano
22	Calalzo di Cadore	1.846299e+07	1.301306	Belluno
23	Tre Ville	1.753142e+07	1.235647	Trento
24	Canale d'Agordo	1.669568e+07	1.176743	Belluno
25	Longarone	1.667863e+07	1.175541	Belluno
26	Funes	1.570032e+07	1.106588	Bolzano
27	Erto e Casso	1.463481e+07	1.031488	Pordenone
28	Pieve di Cadore	1.448860e+07	1.021184	Belluno
29	Molveno	1.444332e+07	1.017992	Trento
30	Ville d'Anaunia	1.204014e+07	0.848612	Trento
31	Vodo Cadore	1.157776e+07	0.816022	Belluno
32	Fiè allo Sciliar	1.090965e+07	0.768933	Bolzano
86	Forni di Sotto	1.088941e+07	0.767506	Udine
33	Sovramonte	1.052010e+07	0.741476	Belluno
34	Rocca Pietore	1.026093e+07	0.723210	Belluno
35	Gosaldo	1.020401e+07	0.719198	Belluno
36	Castelrotto	9.881987e+06	0.696501	Bolzano
37	Spormaggiore	9.489372e+06	0.668829	Trento
38	Borca di Cadore	9.346813e+06	0.658781	Belluno
39	Domegge di Cadore	8.595953e+06	0.605859	Belluno
40	La Valle Agordina	8.377147e+06	0.590437	Belluno
41	Feltre	8.202689e+06	0.578141	Belluno
42	Santa Cristina Valgardena	8.183138e+06	0.576763	Bolzano
43	Stenico	8.043980e+06	0.566955	Trento
44	Predazzo	7.994491e+06	0.563467	Trento
45	La Valle	7.847865e+06	0.553132	Bolzano
46	Selva di Cadore	7.736355e+06	0.545273	Belluno
47	Canazei	7.162718e+06	0.504842	Trento
48	Comelico Superiore	6.373115e+06	0.449189	Belluno
49	Nova Levante	6.319377e+06	0.445401	Bolzano
50	Valdaora	6.246483e+06	0.440264	Bolzano
51	Campodenno	5.441937e+06	0.383558	Trento
52	Perarolo di Cadore	5.338514e+06	0.376268	Belluno
53	Corvara in Badia	5.278604e+06	0.372046	Bolzano
54	Lorenzago di Cadore	4.859123e+06	0.342480	Belluno
55	Mazzin	4.460416e+06	0.314378	Trento
56	Rivamonte Agordino	4.392355e+06	0.309581	Belluno
57	Comano Terme	4.290584e+06	0.302408	Trento
58	Nova Ponente	4.281296e+06	0.301754	Bolzano
59	Falcade	3.908421e+06	0.275473	Belluno
60	Moena	3.532846e+06	0.249002	Trento
61	Livinallongo del Col di Lana	3.297680e+06	0.232427	Belluno
62	Tramonti di Sopra	3.272357e+06	0.230642	Pordenone
63	Alleghe	3.146113e+06	0.221744	Belluno
64	Sporminore	2.988795e+06	0.210656	Trento
65	Voltago Agordino	2.968125e+06	0.209199	Belluno
66	Mezzano	2.873953e+06	0.202562	Trento
67	Agordo	2.783137e+06	0.196161	Belluno
68	Aldino	2.716007e+06	0.191429	Bolzano
69	Belluno	2.709831e+06	0.190994	Belluno
70	San Gregorio nelle Alpi	2.353924e+06	0.165909	Belluno
71	Campitello di Fassa	2.175291e+06	0.153319	Trento
72	Cencenighe Agordino	1.682973e+06	0.118619	Belluno
73	Sagron Mis	1.411328e+06	0.099473	Trento
74	Santa Giustina	1.035951e+06	0.073016	Belluno
75	Lozzo di Cadore	9.988353e+05	0.070400	Belluno
76	Zoppè di Cadore	7.359387e+05	0.051870	Belluno
77	Cavedago	3.482208e+05	0.024543	Trento
78	Soraga di Fassa	3.342176e+05	0.023556	Trento
79	Ortisei	3.171401e+05	0.022353	Bolzano
80	Imer	1.894005e+05	0.013349	Trento
81	San Tomaso Agordino	9.145406e+04	0.006446	Belluno
82	Andalo	3.618142e+04	0.002550	Trento
83	Ospitale di Cadore	1.114393e+04	0.000785	Belluno
84	Colle Santa Lucia	8.043294e+03	0.000567	Belluno